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International Conference on Machine Learning and Data Mining in Pattern Recognition

MLDM 2016: Machine Learning and Data Mining in Pattern Recognition pp 155–169Cite as

Driving Style Identification with Unsupervised Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

One way to optimise insurance prices and policies is to collect and to analyse driving trajectories: sequences of 2D-points, where time distance between any two consequitive points is a constant. Suppose that most of the drivers have safe driving style with similar statistical characteristics. Using above assumption as a main ground, we shall go through the list of all drivers (available in the database) assuming that the current driver is “bad”. We shall add to the training database several randomly selected drivers assuming that they are “good”. By comparing the current driver with a few randomly selected “good” drivers, we estimate the probability that the current driver is bad (or has significant deviations from usual statistical characteristics). Note as a distinguished particular feature of the presented method: it does not require availability of the training labels. The database includes 2736 drivers with 200 variable length driving trajectories each. We tested our model (with competitive results) online during Kaggle-based AXA Drivers Telematics Challenge in 2015.

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Author information

Authors and Affiliations

  1. Department of Mathematical Methods in Economy, Vyatka State University, Kirov, Russia

    Vladimir Nikulin

Authors
  1. Vladimir Nikulin
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Corresponding author

Correspondence to Vladimir Nikulin .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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© 2016 Springer International Publishing Switzerland

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Nikulin, V. (2016). Driving Style Identification with Unsupervised Learning. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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